DOC: Minor docstring tweaks.

etc/requirements_dev.txt

@@ -1,4 +1,5 @@
 # Testing
+coverage==4.0.3
 nose==1.3.7
 nose-parameterized==0.5.0
 nose-ignore-docstring==0.2

zipline/pipeline/data/dataset.py

@@ -100,8 +100,8 @@ class BoundColumn(Term):
         The dtype of data produced when this column is loaded.
     latest : zipline.pipeline.data.Factor or zipline.pipeline.data.Filter
         A Filter/Factor computing the most recently known value of this column
-        on each date. Produces a Filter if self.dtype == np.bool_, otherwise
-        produces a Factor.
+        on each date. Produces a Filter if self.dtype == ``np.bool_``,
+        otherwise produces a Factor.
     dataset : zipline.pipeline.data.DataSet
         The dataset to which this column is bound.
     name : str

zipline/pipeline/factors/events.py

@@ -89,9 +89,18 @@ class BusinessDaysUntilNextEarnings(BusinessDaysUntilNextEvents):
     Factor returning the number of **business days** (not trading days!) until
     the next known earnings date for each asset.
 
+    Assets that announced or will announce earnings on the day of ``compute``
+    will produce a value of 0.0.  Assets that will announce the event on the
+    next upcoming business day will produce a value of 1.0.
+
+    Assets for which the date of the next earnings announcement is ``NaT`` will
+    produce a value of ``NaN``.  This most commonly occurs because many
+    companies do not publish the exact date of their upcoming earnings
+    announcements until a few weeks before the announcement.
+
     See Also
     --------
-    zipline.pipeline.factors.BusinessDaysSincePreviousEarnings
+    BusinessDaysSincePreviousEarnings
     """
     inputs = [EarningsCalendar.next_announcement]
 
@@ -101,9 +110,21 @@ class BusinessDaysSincePreviousEarnings(BusinessDaysSincePreviousEvents):
     Factor returning the number of **business days** (not trading days!) since
     the most recent earnings date for each asset.
 
+    Assets that announced or will announce earnings on the day of ``compute``
+    will produce a value of 0.0.  Assets that will announce the event on the
+    next upcoming business day will produce a value of 1.0.
+
+    Assets which announced or will announce the earnings today will produce a
+    value of 0.0. Assets that announced the on the previous business day will
+    produce a value of 1.0.
+
+    Assets for which the previous earnings date is `NaT` will produce a value
+    of `NaN`.  This will happen in the interval between IPO and first earnings
+    for most companies.
+
     See Also
     --------
-    zipline.pipeline.factors.BusinessDaysUntilNextEarnings
+    BusinessDaysUntilNextEarnings
     """
     inputs = [EarningsCalendar.previous_announcement]
 
@@ -117,7 +138,7 @@ class BusinessDaysSincePreviousCashBuybackAuth(
 
     See Also
     --------
-    zipline.pipeline.factors.BusinessDaysSincePreviousCashBuybackAuth
+    BusinessDaysSincePreviousCashBuybackAuth
     """
     inputs = [CashBuybackAuthorizations.previous_announcement_date]
 
@@ -132,6 +153,6 @@ class BusinessDaysSincePreviousShareBuybackAuth(
 
     See Also
     --------
-    zipline.pipeline.factors.BusinessDaysSincePreviousShareBuybackAuth
+    BusinessDaysSincePreviousShareBuybackAuth
     """
     inputs = [ShareBuybackAuthorizations.previous_announcement_date]

zipline/pipeline/factors/factor.py

@@ -455,10 +455,10 @@ class Factor(CompositeTerm):
             Return True for assets falling below this percentile in the data.
         mask : zipline.pipeline.Filter, optional
             A Filter representing assets to consider when percentile
-            thresholds.  If mask is supplied, percentile cutoffs are computed
-            each day using only assets for which `mask` returns True, and
-            assets not passing `mask` will produce False in the output of this
-            filter as well.
+            calculating thresholds.  If mask is supplied, percentile cutoffs
+            are computed each day using only assets for which ``mask`` returns
+            True.  Assets for which ``mask`` produces False will produce False
+            in the output of this Factor as well.
 
         Returns
         -------

zipline/pipeline/factors/latest.py

@@ -8,6 +8,9 @@ from ..mixins import SingleInputMixin
 class Latest(SingleInputMixin, CustomFactor):
     """
     Factor producing the most recently-known value of `inputs[0]` on each day.
+
+    The `.latest` attribute of DataSet columns returns an instance of this
+    Factor.
     """
     window_length = 1